# Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction

## TL;DR

This paper introduces a flexible framework for ptychographic reconstruction using automatic differentiation, simplifying derivative calculations and enabling versatile modeling of complex experimental setups.

## Contribution

The paper presents a novel approach that replaces analytical derivatives with automatic differentiation for ptychographic imaging, broadening the scope of reconstructive methods.

## Key findings

- Successfully reconstructed objects across various complex models
- Demonstrated the method's generality and flexibility
- Simplified the derivative computation process

## Abstract

Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated from a present guess of the object. Efficient minimization methods require analytical calculation of the derivatives of the error metric, which is not always straightforward. This limits our ability to explore variations of basic imaging approaches. In this paper, we propose to substitute analytical derivative expressions with the automatic differentiation method, whereby we can achieve object reconstruction by specifying only the physics-based experimental forward model. We demonstrate the generality of the proposed method through straightforward object reconstruction for a variety of complex ptychographic experimental models.